Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Software bias is an increasingly important operational concern for software engineers. We
present a large-scale, comprehensive empirical study of 17 representative bias mitigation …

Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …

Software defect prediction is one of the most popular research topics in software
engineering. It aims to predict defect‐prone software modules before defects are discovered …

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction
with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive …

Machine Learning (ML) software can lead to unfair and unethical decisions, making software
fairness bugs an increasingly significant concern for software engineers. However …

Class imbalance is an important problem, encountered in machine learning applications,
where one class (named as, the minority class) has extremely small number of instances …

Software defect prediction aims to identify the potential defects of new software modules in
advance by constructing an effective prediction model. However, the model performance is …